MFGNN-DSA: A Model for Predicting Drug–Side Effect Associations via Multifeature Fusion and Graph Neural Networks
Bibliographic record
Abstract
Predicting associations between drugs and adverse side effects is essential for drug discovery and safety evaluation. Current models predominantly emphasize singular attributes of drugs and side effects, often neglecting to fully encapsulate their multifaceted characteristics and intricate interrelations. Here, we present MFGNN-DSA, a multifeature graph neural network framework that integrates heterogeneous biomedical information to achieve a more accurate prediction. Initially, the model extracts multisource features of drugs and side effects, which are integrated into attribute-based feature vectors via graph sampling and aggregation networks. A heterogeneous network encompassing diseases, drugs, and side effects is then constructed, and the HIN2Vec method is applied to obtain topological feature vectors. Subsequently, these topological, attribute-based, and aggregated feature vectors are processed through a multihead self-attention mechanism to derive the final feature vectors. Ultimately, the concatenated feature vectors are passed through a fully connected layer to predict the probability of drug-side effect association. Experimental results demonstrate that our model outperforms state-of-the-art methods in terms of AUC and AUPR. Case studies offer additional evidence supporting the model's effectiveness. The source code and experimental data of MFGNN-DSA are publicly available at https://github.com/MFGNN/MFGNN-DSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".